The Industrial Internet of Things – Hype or Reality?

Most of you have seen the term IoT – Internet of Things – presented at webinars, seminars and conferences. If not, you have probably read-up on one of the thousands of articles, market reports, blogs et cetera on the topic. Rest assured this post is not about IoT but covers its less known ‘brother’: the Industrial Internet of Things (IIoT).

A definition: The Industrial Internet of Things is the use of IoT technologies in manufacturing with a focus to improve operational efficiency.

To improve operational efficiency you need to

  1. determine what has gone wrong
  2. analyse why things went wrong
  3. understand what is happening now and
  4. predict what will happen.

When I break this down into technology and data needs we get the following:

Result Determine what has gone wrong Analyse why things went wrong Understand what is happening now Predict what will happen
Input Historical data and interpretation Historical data, fault classification and domain knowledge Streaming (sensor) data/events and decision support Streaming (sensor) data/events and ML models trained on historical data
Technology Need Traditional Business Intelligence reports and dashboards Software that supports diagnostic reasoning, replay and simulation Systems with goal oriented HMI, able to execute knowledge base iBPMS/Operational Intelligence Platform & (Big)Data Historians



So …. IIoT is IoT technology mixed with machine learning, (big-)data and stream processing of data and events. The objective to improve operational efficiency which can be reduction of waste, energy usage, quality improvement, extending assets remaining useful lifetime et cetera et cetera.

Hype or Reality

Everyone will sign for (and sigh over) the prospects painted in the Accenture report(1):

“In the future, successful companies will use the Industrial Internet of Things to capture new growth through three approaches: boost revenues by increasing production and creating new hybrid business models, exploit intelligent technologies to fuel innovation, and transform their workforce.”

But I encourage you to read it,  the report provides some nice examples of companies finding new business models and revenue streams, embracing IIoT.

The field of IIoT has been around for many years in the earlier blog:  Invest in Predictive Maintenance the Rolls Royce casus a perfect example. The early adapters were those that paid sometimes dearly to stay ahead of the curve. Reductions in computing costs, sensor costs, upcoming of IoT platforms,  ML suites/algorithms and the availability of M2M, SigFox and LoRa networks are all catalysts for IIoT. These are making the Industrial Internet of Things concept a reality.

A Framework Model

IIoT applications and solutions are driving transformation and are creating a new wave of disruptive companies and solution providers such as Microsoft, ThingWorx, Bosch, Splunk and many others. This in the market place where once only SCADA vendors were active.

A good starting point in deciding which vendor or solution-landscape to adopt, for your IIoT project, is to reflect your requirements/needs to the IoT Reference Model(2):

Level What
7 Collaboration & Processes Involving people and business processes: providing real-time insights, decision support and collaboration options to other areas in the business (e.g. an updated ML model for predicting failures to maintenance).
6 Application Reporting, analytics, control: diagnostic, predictive and prescriptive analytics (preferably autonomous) to control your things. Reporting: accounting, usage, state on your things and application (service).
5 Data Abstraction Aggregation and access: aggregation of data to reports (required usage) and access of data (interface) for applications.
4 Data Accumulation Storage: event persistence, filtering, sampling events, event based rules, event aggregation and complex event processing.
3 Edge (Fog) Computing Data element analyses and transformation: filtering, cleaning and aggregating data, generating events and alarms.
2 Connectivity Communication and processing units: providing security, reliable networking, protocol translation, switching and routing.
1 Physical Devices & Controllers The Things in IoT: generating data (analogue/digital) and events, queried and controlled over the net.

The CISCO IoT Reference Model is really helpful to identify the technology mix you require. In my view however data is not at rest from level 5 onwards but still in motion! (3).


UReason has been at the forefront of IoT /IoE, reasoning over real-time streaming data and events in the manufacturing industry and telecom. We apply an ensemble of techniques – best fitting the requirements – and a wealth of knowledge focused on providing a tailored response to the environment of our customers.

Our capabilities in the Industrial Internet of Things field include:

  • Feasibility studies and Proof of Concepts including hardware prototyping and field tests;
  • Support and roll-out of IIoT solutions in Operational Safety and Predictive Maintenance;
  • Recommendations for human-cyber physical systems, augmented reality and Internet of Things technologies; and
  • Support in Machine Learning and Big Data initiatives supporting IIoT applications.


(1): Accenture Technology: Driving Unconventional Growth through the Industrial Internet of Things, 2015

(2): Cisco, The Internet of Things Reference Model, 2014

(3): Cisco, Building the Internet of Things, Internet of Things World Forum, 2014

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